Method of shutterless non-uniformity correction for infrared imagers

ABSTRACT

A method of correcting an infrared image including a plurality of pixels arranged in an input image array, a first pixel in the plurality of pixels having a first pixel value and one or more neighbor pixel with one or more neighbor pixel values. The first pixel and the one or more neighbor pixels are associated with an object in the image. The method includes providing a correction array having a plurality of correction pixel values, generating a corrected image array by adding the first pixel value to a correction pixel value in the correction array, and detecting edges in the corrected image array. The method also includes masking the detected edges in the corrected image array, updating the correction array, for each correction pixel value in the correction array and providing an output image array based on the correction array and the input image array.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/353,655, filed Nov. 16, 2016, which is a continuation of U.S.application Ser. No. 14/964,165, filed Dec. 9, 2015, now U.S. Pat. No.9,508,124, which is a divisional of U.S. patent application Ser. No.14/211,796, filed Mar. 14, 2014, now U.S. Pat. No. 9,251,595, whichclaims priority to U.S. Provisional Patent Application No. 61/794,200,filed Mar. 15, 2013 entitled “METHOD OF SHUTTERLESS NON-UNIFORMITYCORRECTION FOR INFRARED IMAGERS,” the disclosures of which are herebyincorporated by reference in their entirety for all purposes. Thisapplication is related to U.S. patent application Ser. No. 14/206,297,filed on Mar. 12, 2014, entitled “METHOD AND SYSTEMS FOR PRODUCING ATEMPERATURE MAP OF A SCENE,” and U.S. patent application Ser. No.14/206,341, filed on Mar. 12, 2014, entitled “METHOD AND SYSTEM FORPROVIDING SCENE DATA IN A VIDEO STREAM,” the disclosures of which arehereby incorporated by reference in their entirety for all purposes.

BACKGROUND OF THE INVENTION

The electromagnetic spectrum encompasses radiation from gamma rays,x-rays, ultra violet, a thin region of visible light, infrared,terahertz waves, microwaves, and radio waves, which are all related anddifferentiated in the length of their wave (wavelength). All objects, asa function of their temperatures, emit a certain amount of radiation.For example, the higher an object's temperature, the more infraredradiation is emitted.

Thermal cameras can detect this radiation in a way similar to the way aphotographic camera detects visible light and captures it in aphotograph. Because thermal cameras detect and capture infrared light,thermal cameras can work in complete darkness, as ambient light levelsdo not matter and are not needed. Color thermal cameras require a morecomplex construction to differentiate wavelength and color has lessmeaning outside of the normal visible spectrum because the differingwavelengths do not map uniformly into the system of color visible to andused by humans.

The monochromatic images from infrared cameras are often displayed inpseudo-color, where changes in color are used, as opposed to changes inintensity, to display changes in the signal, for example, gradients oftemperature. This is useful because although humans have much greaterdynamic range in intensity detection than color overall, the ability tosee fine intensity differences in bright areas is fairly limited.Therefore, for use in temperature measurement, the brightest (warmest)parts of the image may be colored white, intermediate temperatures redsand yellows, transitioning to blues and greens, with the dimmest(coolest) parts black. Typically a scale may be shown next to a falsecolor image to relate colors to temperatures.

Thermal cameras have many applications, particularly when light andvisibility are low. For example, thermal cameras have been used inmilitary applications to locate human beings or other warm entities.Warm-blooded animals can also be monitored using thermographic imaging,especially nocturnal animals. Firefighters use thermal imaging to seethrough smoke, find people, and localize hotspots of fires. With thermalimaging, power line maintenance technicians locate overheating jointsand parts, a telltale sign of their failure, to eliminate potentialhazards. Where thermal insulation becomes faulty, building constructiontechnicians can see heat leaks to improve the efficiencies of cooling orheating air-conditioning. Thermal imaging cameras are also installed insome luxury cars to aid the driver at night. Cooled infrared cameras canbe found at major astronomy research telescopes, even those that are notinfrared telescopes.

SUMMARY OF THE INVENTION

According to the present invention, techniques related to a method ofcorrecting non-uniformity in infrared (IR) images is provided. Moreparticularly, embodiments of the present invention relate to methods forcorrecting the drift in an IR image based upon data observed in theimage, without the use of a shutter, one of the industry standardtechniques. The method may utilize factory calibrated tables, such as again table and an offset table. The method may further utilize acorrection table, which may be iteratively updated. These methods canalso assume that any pair of neighboring pixels view the same scenecontent (e.g., object) to compute the correction table at a local level,and when the assumption is violated, use an edge mask and motiondetector to avoid correcting the affected pixels.

One issue generally suffered by thermal cameras is that ofnon-uniformity. That is, each detector (pixel) can have slightlydifferent gain and offset across the array. Accurately compensating forthis non-uniformity is necessary for high-quality thermal imaging.Further complicating this issue is the fact that the offsets inparticular will change or drift with time.

According to a particular embodiment of the present invention, a methodof correcting an infrared image is provided. The method includesproviding a processor and receiving the infrared image from a thermalcamera. The infrared image includes a plurality of pixels arranged in aninput image array, a first pixel in the plurality of pixels having afirst pixel value and one or more neighbor pixel with one or moreneighbor pixel values. The first pixel and the one or more neighborpixels are associated with an object in the image, the one or moreneighbor pixels being adjacent to the first pixel in the input imagearray. The method also includes updating, by the processor, a correctionarray having a plurality of correction pixel values. The correctionarray is updated by, for each correction pixel value: determining theone or more neighbor correction pixel values for the correction value ofthe correction pixel array corresponding to the first pixel value of theinput image array; computing a difference between a first correctionvalue of the first pixel value and a neighbor correction value for eachneighbor pixel value; determining that an absolute value of thedifference is within a range; and determining the correction pixel valuebased on the difference. The method further includes providing an outputimage array based on the correction array and the input image array.

According to another embodiment of the invention, a sum of the firstportion of the difference and the second portion of the difference canbe equal to the difference. In another embodiment of the invention,updating the correction array can include adding the difference, or afraction of the difference, or the first or second portion of thedifference, to a previous neighbor correction value to update a neighborcorrection value, and subtracting the difference (or another portion ofthe difference) from a previous correction value to update a correctionvalue.

In another embodiment of the invention, the method can further includerather than determining that the first pixel value is not an edge,determining that the first pixel is an edge pixel, and assigning a maskto the first pixel.

According to another embodiment of the invention, providing the outputimage table can include summing corresponding pixel values from theinput image array and the correction array.

In another particular embodiment of the invention, the image receivedfrom the camera may be a raw image, and the input image array caninclude multiplying the raw image by a gain array, wherein the gainarray is a predetermined array of values. According to anotherembodiment of the invention, the input image array can further compriseadding an offset array, wherein the offset array is a predeterminedarray of values.

Numerous benefits are achieved by way of the present invention overconventional techniques. For example, embodiments of the presentinvention provide a local correction technique that may propagate overtime to achieve uniformity of the entire array. These and otherembodiments of the invention along with many of its advantages andfeatures are described in more detail in conjunction with the text belowand attached figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified flowchart illustrating a method of correcting aninfrared image according to an embodiment of the present invention;

FIG. 2 is a simplified flowchart illustrating a method of correcting aninfrared image using edge detection according to an embodiment of thepresent invention;

FIG. 3 is a simplified flowchart illustrating a method of updating thecorrection table using edge detection according to an embodiment of thepresent invention;

FIG. 4A is a simplified flowchart illustrating a method of correcting aninfrared image by updating a correction table with and without edgedetection according to another embodiment of the present invention;

FIG. 4B is a simplified flowchart illustrating a method of correcting aninfrared image by updating a correction table without edge detectionaccording to another embodiment of the present invention;

FIG. 5 is a simplified flowchart illustrating a method of updating thecorrection table without edge detection according to an embodiment ofthe present invention;

FIG. 6 is a simplified flowchart illustrating a method of updating thecorrection table without edge detection according to an embodiment ofthe present invention;

FIG. 7 is a simplified flowchart illustrating a method of correcting aninfrared image according to another embodiment of the present invention;

FIG. 8 is an example of input infrared image with drift and itsrespective output corrected infrared image according to embodiments ofthe present invention; and

FIG. 9 is a high level schematic diagram illustrating a subsystem orcomponent according to an embodiment of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention provide a method of correctingnon-uniformity in infrared (IR) images. The method can incorporate acorrection table that, when added to the image, corrects the driftassociated with each pixel.

Infrared imagers may be devices used to capture and/or form infraredimages, for example, a thermographic camera or infrared camera. Infraredimagers may include any device that forms an image using infraredradiation. Instead of the 450-750 nanometer range of the visible lightcamera, infrared cameras may operate in wavelengths as long as 14,000 nm(14 μm).

Infrared energy is a part of the electromagnetic spectrum, whichencompasses radiation from gamma rays, x-rays, ultra violet, a thinregion of visible light, infrared, terahertz waves, microwaves, andradio waves. The various categories of infrared radiation are relatedand differentiated in the length of their wave (wavelength). All objectsemit a certain amount of infrared radiation as a function of theirtemperature.

In general, objects with higher temperatures emit more infraredradiation as black-body radiation. Infrared imagers can detect thisradiation in a way similar to the way an ordinary camera detects visiblelight. Infrared imagers have been used in various applications,particularly in applications with low light, such as in the nighttime,smoke-filled buildings, or underground. Accordingly, infrared imaginghas useful applications in military, rescue, and wildlife observationoperations, for example.

Color cameras require a more complex construction to differentiatewavelength and color has less meaning outside of the normal visiblespectrum because the differing wavelengths do not map uniformly into thesystem of color vision visible and detectable to humans. Infrared imageresolution and refresh rates are typically considerably lower than theresolution and refresh rates of visible cameras.

One existing issue with infrared imagers is that they are extremelysusceptible to temperature variations of the device itself, which causefocal-plane array (FPA) drift; thus as the infrared imager device isbeing operated, the device itself generates heat during its operation,resulting in noise in an infrared image. For example, in uncooleddetectors the temperature differences at the sensor pixels are minute; a1° C. difference at the scene induces just a 0.03° C. difference at thesensor. Therefore, these sensors are particularly sensitive to noise. Inaddition, the pixel response time is also fairly slow, at the range oftens of milliseconds.

Therefore, to reduce the noise in infrared images, some infrared camerasare kept cooled, by containing them in a vacuum-sealed case or Dewar andcryogenically cooled. The cooling is necessary for the operation of thesemiconductor materials in the sensors of the infrared imagers to reduceFPA drift, and the range of operating temperature may range depending ontype and performance level.

Without cooling, these photo-detecting sensors which detect and convertinfrared radiation would be ‘blinded’ or flooded by their own radiation,which results in FPA drift and noise in the thermal image. However, thedrawbacks of cooled infrared cameras are that they are expensive both toproduce and to operate at specific cooled temperatures, as the powerconsumption of cooling is high, costly, and time-intensive. For example,the thermal imager may need several minutes to cool down before it canbegin operating properly. Additionally, even cooled sensors mayexperience some drift, but on a small scale than compared to the driftof an uncooled sensor. However, embodiments of the present invention areapplicable to both cooled and uncooled sensors. Further, drift is more afunction of dark current than of being flooded by self-radiation.

Alternatively, uncooled infrared imagers use a sensor operating atambient temperature, or a sensor stabilized at a temperature close toambient using small temperature control elements, which are morecost-effective to produce and operate. Modern uncooled detectors all usesensors that work by the change of resistance, voltage or current whenheated by infrared radiation. These changes are then measured andcompared to the values at the operating temperature of the sensor.

Uncooled infrared sensors can be stabilized to an operating temperatureto reduce image noise, but they are not cooled to low temperatures anddo not require bulky, expensive cryogenic coolers. This makes infraredcameras smaller and less costly. However, their resolution and imagequality tend to be lower than cooled detectors. So the infrared imager,as it warms up or cools down, each pixel starts to react a little bitdifferently than it did at the initial temperature, consequently, eachpixel begins drift in its performance. This drift, or FPA drift,typically appears as fixed patterns emerging in the image, for example,as a cross-hatched pattern, or as unwanted artifacts in the image.Typical methods of correcting FPA drift in infrared images was to eitherutilize factory calibration tables to help to correct the drift, andalso implement a shutter operation in the infrared imager to generate anoffset for the image. For example, take a picture of the uniform scene,or that shutter, and use that as an offset to correct the non-uniformityof the FPA drift of the pixels.

As described herein, embodiments relate to a shutter-less method ofcorrecting non-uniformity in IR images. The method can determine if animage includes an edge. “Edges” occur around the outline of objects inthe image and manifest within the image as a difference in pixel valuebetween a pixel and one or more of its neighbors (e.g., above, below,left, right, or diagonal, etc.). A small difference in pixel value canbe indicative of a weak edge and a large difference in pixel value canbe indicative of a strong edge. The method may initially assume thateach pixel and its immediate neighbors are viewing the same scenecontent (e.g., object) in an image, and thus should read the same pixelvalue. In an embodiment, an edge can cause these and other assumptionsto be untrue, including the assumption that each pixel and its immediateneighbors are viewing the same object in a scene. In an embodiment ofthe invention, a pixel showing an edge may not be processed (e.g.,skipped).

When neighboring pixels do not read the same value, it can also be dueto drift of the focal plane array (FPA) from the factory calibratednon-uniformity correction (NUC) tables.

In an embodiment, a correction table can be incorporated with the methodto correct the drift. The correction table may be the same size as theimage so that each entry (e.g., cell) in the table can store thecorrection value for the corresponding pixel in the image. Other tables,such as a gain or an offset table, may also be used to iteratively forma correction table. The gain and offset tables may be factory tableswith predetermined settings.

An offset table may be a matrix that is also the same size of the imagesuch that each entry in this table corresponds to one of the pixels inthe image. The offset table may be automatically adjusted to removenon-uniformities from the infrared imager. Gain and offset tables may befactory calibration tables, which may be used in embodiments of thepresent invention without the use of the shutter to capture a uniformimage. Embodiments of the invention describe an automatic algorithm toreplace the shutter using the factory calibration tables for gain andoffset, as well as a correction table.

Although some embodiments of the present invention utilize gain andoffset arrays in conjunction with a linear NUC model, these arrays andtheir use are not required by the present invention. The presentinvention is not dependent upon the implementation of a linear NUCmodel. On the contrary, embodiments of the present invention can beimplemented using other types of NUC models. Accordingly, embodimentscreate a table C such that an output=f(raw image)+C is an image withfixed pattern noise corrected, where f( ) represents a correction of theraw image according to tables previously measured (e.g., in a factoryduring manufacturing). These tables have coefficients for each pixelthat are useful in any NUC model, be that linear or otherwise. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

The method may begin by forming the correction table. Each pixel can beanalyzed by comparing the pixel to each of its neighbors. Any number ofneighbors may be used. For example in the case of four neighbors, ifI(x, y) represents the image pixel at column “x” and row “y,” then whenI(x, y) is processed, I(x+1, y), I(x−1, y), I(x, y+1), and I(x, y−1) arethe neighbors.

The pixel analysis for the correction table can occur in any order. Forexample, the analysis can begin by processing the pixel on the first rowand first column of the image and proceed along the columns of the firstrow. When all columns of that row are finished, processing can move tothe next row, starting at the first column. Processing can continue toeach row until each pixel in the image has been processed.

The correction table can alter the correction value for a variety ofsituations, including when the pixel is part of an edge region. If themethod is allowed to operate on an edge, the edge can become blurry asthe algorithm seeks to make the pixels around the edge read the samevalue.

Edge regions can be detected during processing by implementing an edgedetection algorithm. Correction table entries corresponding to thedetected edge pixels can be prohibited from being updated (e.g., theyare “masked out” of the updating process) in order to avoid adeleterious blurring effect. Any edge detection algorithm may be used(e.g., the Canny algorithm that can detect weak as well as strongedges).

In an embodiment, a motion detection algorithm can also be used whenforming the correction table. Since the edge detection method cannotdetect some weak edges (e.g., locations in the image where only a smalldifference in pixel value exists between a given pixel and its neighbordue to the scene), a motion detection algorithm can be used. The motiondetection algorithm can prohibit the correction table from being updatedwhile the scene is stationary. This can prevent “burn in,” which is acondition where, in some embodiments, the correction factor erroneouslytakes scene differences as pixel offset differences. The motiondetection algorithm may be accomplished with the use of motion sensorsmounted to the camera or may be accomplished with image processingtechniques.

To begin processing the current frame, each pixel at column “x” and row“y” may be initially corrected with the factory tables (e.g., in thecase of a linear non-uniformity model): I(x,y)=g(x,y)P(x,y)+O(x,y),where g(x,y) is the corresponding entry in the factory-determined gaintable, P(x,y) is the pixel value at column “x” and row “y” in the rawobserved image, and O(x,y) is the corresponding entry in thefactory-determined offset table. The pixels may be further modified byadding the corresponding entry in the correction table from the previousframe C(x,y): I′(x,y)=I(x,y)+C(x,y), where I′(x,y) is the initiallycorrected pixel value at column “x” and row “y.” In an embodiment, thecorrection table, C(x,y), can be initialized to all zeros when thecamera is turned on or can be initialized to some other predeterminedarray. If the camera is determined to not be moving, I′(x,y) is outputand no update is made to the correction table on the current frame. Ifthe camera is determined to be moving, the initially corrected imageI′(x,y) is input to the edge detection algorithm.

Once the edges have been detected, updating of the correction tableC(x,y) for the current frame may proceed. Processing the x^(th) columnand the y^(th) row of table C can proceed by first determining if thepixel I(x,y) is an edge pixel. If it is an edge pixel, no processing maybe done and the algorithm can move on to the next pixel. If it is not anedge pixel, the algorithm can compute an update to table C.

To update table C, the algorithm can then proceed entry by entry in thepreviously mentioned order by computing the difference (“DIFF”) betweenthe corrected pixel and the corrected value of its first neighbor, ifthe neighbor is not an edge pixel. For example, if the neighbor isI(x+1,y), then: DIFF=(I(x,y)+C(x,y))−(I(x+1,y)+C(x+1,y)). If theneighbor is an edge pixel, that neighbor can be ignored.

A portion of “DIFF” can be subtracted from the pixel's correspondingentry in the correction table C(x,y), and the same portion can be addedto the neighbor's corresponding entry in the correction table C(x+1,y).This process can be repeated for each neighbor. In an embodiment, thealgorithm may be altered to adjust the proportion of the difference thatis added and subtracted from the entries in the correction table.

After all neighbors have been analyzed, processing can proceed to thenext pixel. In an embodiment, the processing can recursively analyzeeach pixel in the received image.

After the correction table has been updated, the updated correctiontable can be added to the NUC-corrected image, using the followingformula: Output(x,y)=I(x,y)+C(x,y).

In one embodiment of the invention, a method of updating the correctiontable with edge detection is provided which may be used with cameramotion and is thus, a motion-based method with edge detection. Thismotion-based method for updating the correction table includes prioredge detection. Further, in this motion-based method with edgedetection, the DIFF is divided by a denominator, in conjunction with thecamera motion detected prevents over-reacting and over-compensation indetecting weak scene edges and/or temporal noise.

In another embodiment of the invention, a method of updating thecorrection table without edge detection is provided which may be usedwith or without camera motion. Thus, this method may be motionless ormotion-based in updating the correction table; however, this method doesnot utilize prior edge detection. In this method of updating thecorrection table without edge detection, it may be determined whetherthe DIFF falls within a range designated by threshold values T1 and T2,which may be set to accommodate a moving camera or a non-moving camera.For example, T1 may be set to a larger value for a moving cameracompared to a non-moving camera, and T2 may be set to a smaller valuefor a moving camera compared to a non-moving camera.

FIG. 1 is a simplified flowchart illustrating a method of correcting aninfrared image according to an embodiment of the present invention. Themethod includes receiving a raw image from a camera at 110. The imagecan comprise a first pixel with a first pixel value and a neighbor pixelwith a neighbor pixel value, wherein the first pixel and the neighborpixel are assumed to view the same object in the image. The raw imagemay be processed using the factory calibration tables for gain andoffset to generate a processed image. The raw image may also beprocessed with a correction table from the previous frame. Thus, forexample, if the camera is receiving the first frame, then the values inthe initial correction table may be zero or another initialpre-determined value, since there is no previous frame to begincalculating a correction table from; in this case.

At 112, the method further includes determining whether the camera(e.g., infrared imager) is moving. When the camera is determined to bemoving, the method further includes detecting the edges in the correctedimage at 114 by iterating through each pixel in the corrected image.Then at 116, the correction table is updated and a corrected image isgenerated with the updated correction table. The corrected image may begenerated by adding the first pixel value to its corresponding correctedpixel value in the updated correction table. The correction table may beupdated, for example, by iterating through each pixel, computing thedifference between the first image pixel and the neighbor pixel value,subtracting the difference from the first image pixel, and adding thedifference to the neighbor pixel value.

Once the correction table is updated, the method includes outputting acorrected image at 118. The corrected image is the processed imagecorrected by the values in the updated correction table updated in 116,which may be achieved by summing corresponding pixel values from theprocessed image and the correction table. The method then proceeds toreceive and process the next raw image at 110, and the previouslyupdated correction table is used for the next frame. When at 112, thecamera is determined to not be moving, in an embodiment, the method maydirectly output the corrected image at 118 with the previous correctiontable.

It should be appreciated that the specific steps illustrated in FIG. 1provide a particular method of correcting an infrared image according toan embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 1 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 2 is a simplified flowchart illustrating a method of correcting aninfrared image according to an embodiment of the present invention. At202, a processed image “I” may be generated based on a received rawimage “P”. Raw image P is processed with a factory calibration table “g”(hereinafter, referred to as “the gain table”), which is the same sizeas the raw image P. Accordingly, each value in the gain table g is again value for a corresponding pixel value in image P. Since P is theraw image captured by the camera, the pixels in P have non-uniformities.“O” is a factory calibrated table for offset (hereinafter, referred toas “the offset table”). As shown in 202, I is a processed image of rawimage P using the gain table g and the offset table O, both of which arefactory calibrated tables. Accordingly, the processed image I is thegain table g multiplied by the raw image P, plus the offset table O,(I=gP+O) which results in some corrections to the non-uniformities ofraw image P, but is not ideal.

It should be noted that I=gP+O is a linear model for NUC that may beused and is described in the embodiment of FIG. 2. However, other NUCmodels may be utilized, for example, quadratic models may be implementedin other embodiments of the invention.

To improve the corrections to the non-uniformities of raw image P, acorrection table “C” may be further added. In addition to processedimage I, there may also be a corrected image “I′” generated, which isthe processed image summed with the correction table (I′=I+C). Similarto g and O, C is a table of the same size as P, however C is not astatic factory calibrated table, but a dynamically generated correctiontable. According to embodiments in the invention, C is populatediteratively to automatically correct I and remove all remainingnon-uniformities that exist after the use of factory calibrated tabled gand O. Thus initially at the first frame to be processed in 202, C maybe set to have values of zero or another pre-determined value, and whenthe next frame is processed, the C used to correct the second frame isupdated and calculated based on the first (or previous) frame.Accordingly, C is constantly and iteratively updated sequentially witheach frame.

At 204, a camera motion detection is performed, which may beaccomplished using a motion sensor, or using image processing-basedtechniques to determine whether the camera is moving. At 206, if thecamera is not moving, then at 214, the output image generated is I′,which is the processed image I summed with C from the previous frame. Ifat 206, the camera is determined to be moving, then at 208, an edgedetection is performed using I′, which is the corrected image using C asgenerated from the last frame.

The edge detection at 208 is performed using the best data currentlyavailable for that frame, which is I′. Therefore the pixels in I′ areanalyzed to determine a list of the edge pixels. An edge of the imagewould be, for example, in a scene including a hot ball against a coldbackground, the edges would just be the outline of that ball. The edgedetection in 208 would result in a list of the pixels that define theoutline of the ball. However, it may not be simple to determine all ofthe edges in the image. Strong edges, for example, such as the ballagainst a white background, may be easy to determine. However weakedges, such as the hot ball in a shadow or against a background in asimilar temperature as the ball, may be more difficult to discern as theedge pixels may start to get lost in the noise of the image. Variousedge detection algorithms and techniques may be implemented. In anembodiment of the invention, the edge detection of 208 determines strongedges and provides a list of edge pixels that define strong edges in theimage.

At 210, the correction values in C may be updated based on the non-edgepixels of I. As shown in 210, an example of the pseudo-code to update Cis shown in nested loops. For each entry (pixel) at x,y of C, it isdetermined whether that entry (x,y) in I is an edge. If I(x,y) is anedge, then that entry (e.g., pixel) is skipped and the next entry in Cand its corresponding entry in I is evaluated. The next entry evaluatedin a table, such as C, g, P, I, and I′, may be determined by scanning inraster scan mode. For example, evaluating all the x values in row y, forexample: (x, y), (x+1, y), (x+2, y) . . . (x+n, y), and then proceedingto the next row, y+1, for example: (x, y+1), (x+1, y+1), (x+2, y+1) . .. (x+n, y+1). The elements of correction table C may be evaluated in anyorder.

If in 210, I(x,y) is not an edge, then the neighbors of that pixel areevaluated. Neighbors may include the pixels directly above, directlybelow, directly to the left, and directly to the right. Neighbors mayalso include pixels that are diagonal—northwest, northeast, southwest,southeast to I(x,y), or any combination of surrounding pixels to I(x,y).

The neighboring pixels are then evaluated to determine if theneighboring pixels are edges or not. If the neighbor pixel is not anedge, then the difference “DIFF” is computed, which may be thedifference between the corrected pixel and the corrected value of itsfirst neighbor. For example, if the neighbor is I(x+1,y), then:DIFF=(I(x,y)+C(x,y))−(I(x+1,y)+C(x+1,y)). Reducing the equation, theDIFF is essentially the best correction of the current pixel minus thebest correction of the neighbor. If the neighbor is an edge pixel, thatneighbor can be ignored.

If the neighbor is not an edge, then ideally, the neighbor and thecurrent pixel I(x,y) should be equal, and DIFF represents the extent towhich the neighbor and the current pixel are not equal. However, DIFFincludes pixel differences due not only to the fixed pattern noise frompixel drift but also from other causes such as temporal noise and weakedges from the scene that did not get masked. Therefore the correctionvalue for I(x,y) is determined by using a portion of DIFF so that overseveral frames, the difference between the pixels will converge to zeroand make the non-edge neighbor and the non-edge current pixel I(x,y)equal without reacting too strongly to temporal noise or weak edges. Asshown in 210, the correction values for I(x,y) and I(neighbor) areupdated in correction table C. To update the correction values in C, aportion of DIFF may be subtracted from the current value of C(x,y),which is the correction value of I(x,y), and added to the currentcorrection value of the neighbor pixel, which is C(neighbor). Everypixel in I and its corresponding entry in C is evaluated to iterativelyupdate all the correction values in every entry in C.

Masking of scene edges found via edge detection is performed to avoidprocessing edge pixels because the correction values are computed tominimize the difference between a current pixel and its neighbor. If theneighbor is an edge and the current pixel is not, then minimizing thedifference would “burn” the edge into table C. This would have theeffect of blurring the edge until the camera moves and it would have theeffect of a ghost edge in that location of the image after the cameramoves. However, edge detection algorithms cannot be counted on to findall edges, particularly weak edges. Therefore, the updates to C are onlyallowed when the camera is moving. Because, if for example, in comparingthe pixel to its neighbor it happens to be on a weak edge that is notmasked out, the correction values will get updated to minimize thedifference across this edge. However, since the update is only allowedif the camera is moving, and since only a portion of DIFF is used toupdate table C, the correction that was erroneously computed because ofthat weak edge will be imperceptibly small and will only be used oncebecause a weak edge in one frame will be positioned on different pixelson the next frame when the camera is moving.

When the camera is not moving, no update to table C is made and I′ (theimage corrected by the last frame's correction table) is output in oneembodiment. Not updating table C in this case keeps unmasked weak edgesfrom being burned into table C. The edge detection in 208 may be usedwhen the camera is moving because weak edges may be difficult to detect.Therefore, if the camera is stationary, and if a pixel and its neighbor,on a current frame form a weak edge, the iterative correction tableupdated in 210 of FIG. 2 may result in a blurring of the edge after theprocessing of several frames.

Thus, in an embodiment, when camera motion is detected in 206, a methodof updating table C with edge detection is performed in 208 and 210.

Alternatively, in another embodiment, when camera movement is detectedin 206, then in 218, a method of updating table C without edge detectionmay be utilized. This method of updating table C in 218 is shown inblock 504 of FIG. 5. Referring to 504 of FIG. 5, without edge detection,every entry in C and every pixel in I is evaluated at 504. For eachcorrection value (x,y) in C, and for each neighbor of (x,y) in C, adifference DIFF is computed. As mentioned previously, every correctionvalue in C corresponds to a pixel in I, for example correction valueC(x,y) corresponds to pixel I(x,y), and correction value C(neighbor)corresponds to pixel I(neighbor).

The DIFF may be computed by taking the difference between I(x,y)+C(x,y)and I(neighbor)+C(neighbor). If the absolute value of DIFF is within arange of values as defined by thresholds T1 and T2, then the correctionvalue is updated. For example, whether T1 >absolute value of DIFF>T2.Thresholds T1 and T2 may be predetermined values. The correction valueC(x,y) for the current pixel is updated by subtracting a fraction of theDIFF from the current correction value. The fraction of the DIFF may becomputed by dividing DIFF by a denominator, denom. The denominatordenom, T1, and T2 may be input values that are set, and/or may bepredetermined. The correction value of the neighbor C(neighbor) may beupdated by adding the fraction of the DIFF (for example, DIFF/denom) tothe current correction value of the neighbor. 504 is repeatediteratively for every entry in C, until the entire table is populatedand updated.

For this moving camera case, T1 may be chosen to be a value such thatthe amount DIFF/denom is imperceptible to the eye when the pixeldifferences (DIFF) is less than T1. T2 may be chosen to be smaller thanT1 such that pixel differences less than T2 are so small as to not beworth correcting. T2 may provide a lower limit on pixel differences inorder to prevent over-convergence of the algorithm.

Referring to FIG. 2, at 212, after every entry in I and C have beeniterated through by either a combination of 208 and 210 (method ofupdating table C with edge detection), or by 218 only (method ofupdating table C without edge detection), then the outputted image is asummation of the processed image I (in which I=gP+O) and the updatedcorrection table C. As a result, the outputted image contains a morecomplete correction of the non-uniformities of the raw image, with thecorrection becoming progressively and iteratively improved.

It should be appreciated that the specific steps illustrated in FIG. 2provide a particular method of correcting an infrared image according toan embodiment of the present invention.

Other sequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 2 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 3 is a simplified flowchart diagram illustrating a method ofupdating a correction table with edge detection according to anembodiment of the present invention, for example, as shown in 210 ofFIG. 2. At 301, a current pixel is provided. Evaluating the currentpixel I(x,y), at 302, the method further includes determining whetherthe current pixel I(x,y) is an edge. If the current pixel is determinedto be an edge at 302, then the method proceeds to 312 and iterates to anext pixel in the image and a next value in the correction table. IfI(x,y) is not an edge, then the neighbors of that pixel are determinedand considered at 304. Neighbors may include any surrounding pixels toI(x,y).

At 306, the neighboring pixels are then evaluated to determine if theneighboring pixels are edges or not. If the neighbor pixel is determinedto be an edge at 306, then the method reverts back to 304 to consideranother neighbor pixel of the current pixel I(x,y) until all theneighboring pixels have been considered. If the neighbor pixel is not anedge, then the difference “DIFF” between the corrected pixel and thecorrected value of its first neighbor is computed at 308. For example,if the neighbor is I(x+1,y), then:DIFF=(I(x,y)+C(x,y))−(I(x+1,y)+C(x+1,y)). If the neighbor is an edgepixel, that neighbor can be ignored.

When the neighbor is not an edge, then DIFF quantifies how different theneighbor and the current pixel I(x,y) are, and the correction value forthe current pixel is calculated to minimize DIFF such that is convergesclose to zero. When DIFF converges, then the current pixel and theneighbor should be almost equal. In 310, the updated correction valuesin C are computed by subtracting DIFF/denom from the current value ofC(x,y), which is the correction value for I(x,y), and adding DIFF/denomto the current correction value of the neighbor pixel, which isC(neighbor). The value of denom may be a predetermined value.

At 312, the method includes proceeding to the next pixel in image I andits corresponding correction value in correction table C. The method maythen proceed to evaluate the next pixel as the current pixel provided in301. The process shown in 301-312 is repeated for every pixel in I(x,y)to iteratively, automatically, and incrementally update the correctionvalues in correction table C.

It should be appreciated that the specific steps illustrated in FIG. 3provide a particular method of updating a correction table according toan embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 3 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 4A is a simplified flowchart illustrating a method of correcting aninfrared image according to another embodiment of the present invention.

At 402, a processed image I may be generated by multiplying a raw imageP with a factory calibrated gain table g with an added factorycalibrated offset table O, such that I=gP+O. As a result, I containssome corrections to the non-uniformities of raw image P, but is notideal, as the corrections are based purely on factory calibrated tableswith predetermined values. In addition to processed image I, there mayalso be a corrected image “I′” generated at 403, which is the processedimage summed with the correction table (I′=I+C). Similar to g and O, Cis a table of the same size as P, however C is not a static factorycalibrated table, but a dynamically generated correction table.According to embodiments in the invention, C is populated iteratively toautomatically correct I and remove all remaining non-uniformities thatexist after the use of factory calibrated tabled g and O. Thus initiallyat the first frame to be processed in 402, C may be set to have valuesof zero or another pre-determined value, and when the next frame isprocessed, the C used to correct the second frame is updated andcalculated based on the first (or previous) frame.

At 404, a camera motion detection operation is performed. Detectingcamera motion may be accomplished using a motion sensor, or using imageprocessing-based techniques. At 406, if the camera is moving, then edgedetection using I′ is performed at 411, and a correction table isgenerated, populated, and updated at 412, using the method illustratedin FIG. 3 and at 210 of FIG. 2. The edge detection at 411 may beperformed using the best data currently available for that frame, whichis I′. Therefore the pixels in I′ are analyzed to determine a list ofthe edge pixels. Various edge detection algorithms and techniques may beimplemented at 411. In an embodiment of the invention, the edgedetection of 411 may provide a list of edge pixels that define strongedges in the image.

However, if at 406, the camera is not moving, then a correction table isgenerated, populated, and updated at 408 according to a methodillustrated in FIG. 6 and at 504 in FIG. 5. Referring to 504 of FIG. 5,without edge detection, every entry in C and every pixel in I. For eachcorrection value (x,y) in C, and for each neighbor of (x,y) in C, adifference DIFF is computed. As mentioned previously, every correctionvalue in C corresponds to a pixel in I, for example correction valueC(x,y) corresponds to pixel I(x,y), and correction value C(neighbor)corresponds to pixel I(neighbor).

The DIFF may be computed by taking the difference between I(x,y)+C(x,y)and I(neighbor)+C(neighbor). If the absolute value of DIFF is within arange of values as defined by thresholds T1 and T2, then the correctionvalue is updated. For example, whether T1 >absolute value of DIFF>T2.Thresholds T1 and T2 may be predetermined values. The correction valueC(x,y) for the current pixel is updated by subtracting a fraction of theDIFF from the current correction value. The fraction of the DIFF may becomputed by dividing DIFF by a denominator, denom. The denominatordenom, T1, and T2 may be input values that are set, and/or may bepredetermined. The correction value of the neighbor C(neighbor) may beupdated by adding the fraction of the DIFF (for example, DIFF/denom) tothe current correction value of the neighbor. 504 is repeatediteratively for every entry in C, until the entire table is populatedand updated.

In this non-moving camera case, T1 may be chosen to be a value such thatpixel differences less than T1 are imperceptible to the eye. T2 may bechosen to be smaller than T1 such that pixel differences less than T2are so small as to not be worth correcting. T2 may provide a lower limiton pixel differences in order to prevent over-convergence of thealgorithm.

Referring back to FIG. 4A, when the correction table C is complete fromeither 408 or 412, it is added to processed image I (I=gP+O) to createan output image that is corrected of its non-uniformities without edgedetection.

It should be appreciated that the specific steps illustrated in FIG. 4Aprovide a particular method of correcting an infrared image according toan embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 4 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 4B is a simplified flowchart illustrating a method of correcting aninfrared image according to another embodiment of the present inventionwithout edge detection for both motionless and motion-based algorithmsfor updating the correction table.

At 452, a processed image I may be generated by multiplying a raw imageP with a factory calibrated gain table g with an added factorycalibrated offset table O, such that I=gP+O.

At 454, a camera motion detection operation is performed. Detectingcamera motion may be accomplished using a motion sensor, or using imageprocessing-based techniques. At 456, if the camera is moving, then acorrection table is generated, populated, and updated at 462 accordingto a method illustrated in FIG. 6 and at 504 in FIG. 5, where T1 islarger and T2 is smaller than in a motionless algorithm of 458.Referring to 504 of FIG. 5, without edge detection, every entry in C andevery pixel in I is evaluated. For each correction value (x,y) in C, andfor each neighbor of (x,y) in C, a difference DIFF is computed.

The DIFF may be computed by taking the difference between I(x,y)+C(x,y)and I(neighbor)+C(neighbor). If the absolute value of DIFF is within arange of values as defined by thresholds T1 and T2, then the correctionvalue is updated. For example, whether T1 >absolute value of DIFF>T2.Thresholds T1 and T2 may be predetermined values. The correction valueC(x,y) for the current pixel is updated by subtracting a fraction of theDIFF from the current correction value. The fraction of the DIFF may becomputed by dividing DIFF by a denominator, denom. The denominatordenom, T1, and T2 may be input values that are set, and/or may bepredetermined. The correction value of the neighbor C(neighbor) may beupdated by adding the fraction of the DIFF (for example, DIFF/denom) tothe current correction value of the neighbor. 504 is repeatediteratively for every entry in C, until the entire table is populatedand updated.

For this moving camera case, T1 may be chosen to be a value such thatthe amount DIFF/denom is imperceptible to the eye when the pixeldifferences (DIFF) is less than T1. T2 may be chosen to be smaller thanT1 such that pixel differences less than T2 are so small as to not beworth correcting. T2 may provide a lower limit on pixel differences inorder to prevent over-convergence of the algorithm.

Referring back to FIG. 4B, when the camera is determined to not bemoving in 456, then the method proceeds to the motionless algorithm of458, according to a method illustrated in FIG. 6 and block 504 of FIG.5, where T1 is smaller and T2 is larger than in the motion-basedalgorithm of 462. Thus, embodiments of the present invention provide amethod of performing NUC in situations when the camera is not moving(i.e., still camera case) by making updates to the correction table whenthe camera is still using block 458 of FIG. 4B.

At 460, when the correction table C is complete from either 458 or 462,it is added to processed image I (I=gP+O) to create an output image thatis corrected of its non-uniformities without edge detection.

It should be appreciated that the specific steps illustrated in FIG. 4Bprovide a particular method of correcting an infrared image according toan embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 4B may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 5 illustrates a simplified flowchart illustrating a method ofupdating a correction table without camera movement according to anembodiment of the invention. At 502, a processed image I=gP+O is used toiteratively determine correction values for correction table C. Withoutedge detection, every entry in C and every pixel in I is evaluated at504. For each correction value (x,y) in C, and for each neighbor of(x,y) in C, a difference DIFF is computed. As mentioned previously,every correction value in C corresponds to a pixel in I, for examplecorrection value C(x,y) corresponds to pixel I(x,y), and correctionvalue C(neighbor) corresponds to pixel I(neighbor).

The DIFF may be computed by taking the difference between I(x,y)+C(x,y)and I(neighbor)+C(neighbor). If the absolute value of DIFF is within arange of values as defined by thresholds T1 and T2, then the correctionvalue is updated. For example, whether T1 >absolute value of DIFF>T2.Thresholds T1 and T2 may be predetermined values. The correction valueC(x,y) for the current pixel is updated by subtracting a fraction of theDIFF from the current correction value. The fraction of the DIFF may becomputed by dividing DIFF by a denominator, denom. The denominatordenom, T1, and T2 may be input values that are set, and/or may bepredetermined. The correction value of the neighbor C(neighbor) may beupdated by adding the fraction of the DIFF (for example, DIFF/denom) tothe current correction value of the neighbor. 504 is repeatediteratively for every entry in C, until the entire table is populatedand updated.

When 504 is applied to a non-moving camera case, T1 is chosen to be avalue such that pixel differences (DIFF) less than T1 are imperceptibleto the eye. When 504 is applied to a moving camera case, T1 may bechosen to be a value such that the amount DIFF/denom is imperceptible tothe eye when the pixel differences (DIFF) is less than T1. T2 is chosento be smaller than T1 such that pixel differences less than T2 are sosmall as to not be worth correcting. T2 provides a lower limit on pixeldifferences in order to prevent over-convergence of the algorithm. Usinga fraction of DIFF may compensate for temporal noise and the lack ofedge detection, particularly for weak edges, because if the correctionvalues get updated on a pixel and a neighbor that form a weak edge, thefractional DIFF can make the correction to the weak edge unnoticeable.In the alternative, as described in other embodiments using cameramovement and edge detection, for weak edges, updating the correctionvalues for a pixel and its neighbor forming a weak edge may result inblurring the edge even further.

According to an embodiment of the present invention, when the camera ismoving, a weak edge that existed on one frame I may not be there or mayhave moved on frame I+1, so the edges may not be blurred, and thecorrection table update process using edge detection techniquesdescribed herein may be used. On the other hand, if the camera isstationary, and if a pixel and its neighbor, on this frame, form a weakedge, the iterative correction table update method may not noticeablyblur the edge since the correction values are updated by fractions ofDIFF.

Then at 506, when the correction table C is complete, it is added toprocessed image I from 502 to result in an output image with itsnon-uniformities corrected without camera movement or edge detection.

It should be appreciated that the specific steps illustrated in FIG. 5provide a particular method of updating a correction table when thecamera is stationary according to an embodiment of the presentinvention. Other sequences of steps may also be performed according toalternative embodiments. For example, alternative embodiments of thepresent invention may perform the steps outlined above in a differentorder. Moreover, the individual steps illustrated in FIG. 5 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 6 illustrates a simplified flowchart diagram of updating acorrection table without edge detection according to an embodiment ofthe present invention, for example, as shown at 504 of FIG. 5. Themethod illustrated in FIG. 6 may be used for both motion-based andmotionless algorithms to update correction table C.

At 601, a current pixel I(x,y) is provided. Subsequently at 602,neighbor correction values of C(x,y) are considered and determined,where C(x,y) is a correction value for the current pixel I(x,y). Themethod considers each neighbor of the current pixel before consideringthe next current pixel as described herein. Thus, each neighbor pixel ofthe current pixel is considered as shown in block 504 of FIG. 5.

At 604, the difference between the correction value for the currentpixel and the correction value of its neighbor is determined as DIFF.The DIFF may be computed by taking the difference between I(x,y)+C(x,y)and I(neighbor)+C(neighbor). At 606, it is determined whether theabsolute value of DIFF is within a range of values, for example, in arange between T2 and T1, with T2 being a lower bound and T1 being anupper bound. If NO, that is, the DIFF is not within the predeterminedrange defined by T1 and T2, then the method makes no update to table Cand proceeds to the next neighbor (No path from 606 to 602). After allneighbors have been visited, the method proceeds to the next pixel asthe new “current pixel” and proceeds again beginning at block 602.

If YES, the DIFF is within the range, to update the correction values,at 608, the correction value C(x,y) for the current pixel is updated bysubtracting a fraction of the DIFF from the current correction value.The fraction of the DIFF may be DIFF divided by a denominator. At 610,the correction value of the neighbor C(neighbor) may be updated byadding the fraction of the DIFF (for example, DIFF/denom) to the currentcorrection value of the neighbor.

Then, at 612, the next correction value in the correction table isevaluated, and its neighbor correction values are determined in 602, inwhich 602-612 are repeated iteratively for every entry in C, until theentire table is populated and updated.

In the embodiment shown in FIGS. 4-6, the infrared image correction isperformed without an edge detection first before updating the correctiontable. Instead it determines DIFF, which is the difference between thepixel and its neighbor. If that difference DIFF is less than thresholdT1 and greater than another threshold T2, then the correction value isupdated. For the non-moving camera case, the thresholds T1 and T2 areset very close such that the range for DIFF is very small. Therefore, asshown in FIG. 4B, when the camera is not moving, the correction tablemay still be incrementally updated to produce a fairly clean image to atleast prevent the fixed-pattern noise from evolving and negativelyaffecting the image.

For the non-moving camera case, typical values for T1 and T2 may be 5-6grayscale values for a 14-bit camera. Input values for T1 and T2 woulddepend on the bit depths of the camera. For example, for 14-bits therewould be 16,384 grayscale values; thus the range between T1 and T2 wouldbe 5-6 grayscale values out of 16,384. Although the camera is 14 bits,the camera is not necessarily detecting everything from zero up to16,383, but may only be detecting just a small range within that totalrange. Therefore, these thresholds of 5-6 grayscale values are set smallenough such that edges having these differences would be undetectable bythe human eye. The fixed-pattern noise starts from zero, but as thecamera heats up or cools down, the fixed pattern noise increases alittle bit each time. These thresholds allow the fixed pattern noise tobe corrected as it evolves and before it becomes perceptible.

For the moving camera case, T1 may be chosen to be a value such that theamount DIFF/denom is imperceptible to the eye when the pixel differences(DIFF) is less than T1.

Accordingly, the correction table update process of FIGS. 5-6 helps toreduce fixed pattern noise. Because no edge detection is performed, someweak edges may be inadvertently updated, but only weak edges that areimperceptible to the human eye. In contrast, strong edges that aredetectable to the human eye typically have a DIFF value of thousands ofgrayscale values.

It should be appreciated that the specific steps illustrated in FIG. 6provide a particular method of updating a correction table when a camerais stationary according to an embodiment of the present invention. Othersequences of steps may also be performed according to alternativeembodiments. For example, alternative embodiments of the presentinvention may perform the steps outlined above in a different order.Moreover, the individual steps illustrated in FIG. 6 may includemultiple sub-steps that may be performed in various sequences asappropriate to the individual step. Furthermore, additional steps may beadded or removed depending on the particular applications. One ofordinary skill in the art would recognize many variations,modifications, and alternatives.

FIG. 7 illustrates a flowchart of a method of correcting infrared imagesaccording to another embodiment of the invention. The method may receivea processed image I at 702, which comprises multiplying a raw image Pwith a gain table g and adding an offset table O. The gain table andoffset table may be the same size as the raw image, such that each entryin the gain table corresponds to an entry in the offset and a pixel inthe raw image.

At 704, the method includes determining whether I is the first frame. Ifit is the first frame, then the method includes, at 706, initializing acorrection table C, which is also the same size as raw image P, the gaintable g, and the offset table O. In an embodiment, correction table Cmay be initialized to have all the entries be zero's or to somepredetermined array of values. Other parameters, such as a mean value ofthe processed image or a counter may also be set to predeterminedvalues. For example, meanImage may be initialized to I=gP+O andmean_image_cntr may be set to 0. After the correction table C and otherparameters are initialized, then the method proceeds to 708 to determinewhether the camera is in motion.

If the current I is not the first frame, the method directly proceeds to708 to determine whether the camera is in motion. If the camera isdetermined to be moving at 708, then the method proceeds to 710 todetermine whether there is motion>T6 where T6 is a measure of cameramotion. For example, if a motion detector is used to measure cameramotion, T6 might be an accelerometer reading. If image processingtechniques are used to detect camera motion, T6 might be in units ofpixels. If motion>T6, then the method further includes, at 712, using amotion-based algorithm to update the correction table C. For example,the motion-based algorithm used may use prior edge detection asillustrated in FIG. 3, and blocks 208 and 210 of FIG. 2. In anotherembodiment, the motion-based algorithm may be implemented without edgedetection, for example, as illustrated in FIG. 6 and block 504 of FIG.5. The correction table C is then added to processed image I to resultin corrected output image at 714.

If there is small motion<T6 determined at 710, then the method proceedsto 716 such that the mean image parameter is reset to I (=gP+O) and themean image counter is reset to 0.

Alternatively, if at 708, the camera is determined to be stationary,then at 722, the method can further include computing a percentagechange in greyscale value of each pixel relative to the correspondingpixel in the mean image (e.g., meanImage). Then at 724, any pixel havinga percentage change greater than a threshold T3 is marked as moving. T3may be a predetermined threshold. The number of pixels marked as movingare counted at 726 and saved as the number of pixels that have changed.

At 728, when the number of changed pixels has exceeded a threshold T4,the mean image parameter may then be reset to I (=gP+O) (e.g.,meanImage=I) and the mean image counter may be reset to 0 (e.g.,mean_Image_cntr=0). Alternatively, if the number of changed pixels hasnot exceed the threshold T4, then at 730, the mean image value is set tothe sum of the processed image I multiplied by a constant α and afraction of the previous mean image value, where constant α is less than1, and the fraction is 1−α. For example, meanImage=αI+(1−α)meanImage.After the mean image value is updated, then the mean image counterparameter is incremented at 732. T4 and α may be predetermined values.

If the mean image counter (e.g., mean_Image_cntr) is greater than athreshold value T5, then at 720 a motionless algorithm without edgedetection is used to update the correction table C. For example,implementing the method illustrated in FIG. 6 and block 504 of FIG. 5 ofupdating correction table C without edge detection, where the inputimage is meanImage instead of I. When the correction table C is updated,then it is added to processed image I to output corrected output imageat 714. If the mean image counter parameter is less than the thresholdT5, then at 714, the processed image I is added to the previouscorrection table C to result in corrected output image. Thresholds T4,T5, and T6 may be predetermined values.

It should be appreciated that the specific steps illustrated in FIG. 7provide a particular method of correcting an infrared image according toan embodiment of the present invention. Other sequences of steps mayalso be performed according to alternative embodiments. For example,alternative embodiments of the present invention may perform the stepsoutlined above in a different order. Moreover, the individual stepsillustrated in FIG. 7 may include multiple sub-steps that may beperformed in various sequences as appropriate to the individual step.Furthermore, additional steps may be added or removed depending on theparticular applications. One of ordinary skill in the art wouldrecognize many variations, modifications, and alternatives.

FIG. 8 illustrates an example of an input image and its correspondingoutput image after correction methods performed according to embodimentsof the invention. 802 is an input image of an actual infrared image withFPA drift, which can be observed as a cross-hatched pattern in theimage. When 802 is processed using correction methods described in thepresent application, the result is 804, where the cross-hatched patternof the FPA drift is significantly and visibly reduced.

FIG. 9 is a high level schematic diagram illustrating a data processingsystem upon which the disclosed embodiments may be implemented incertain embodiments. Embodiments may be practiced with various computersystem configurations such as infrared cameras, hand-held devices,microprocessor systems, microprocessor-based or programmable userelectronics, minicomputers, mainframe computers and the like. As anexample, the data processing system can be used to correct an infraredimage in conjunction with an infrared lens system 920 as describedthroughout the present application, for example, providing for controlof the imaging functions of the infrared lens system 920. Theembodiments can also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a wire-based or wireless network. FIG. 9 shows one example of adata processing system, such as data processing system 900, which may beused with the present described embodiments. Note that while FIG. 9illustrates various components of a data processing system, it is notintended to represent any particular architecture or manner ofinterconnecting the components as such details are not germane to thetechniques described herein. It will also be appreciated that networkcomputers and other data processing systems which have fewer componentsor perhaps more components may also be used. The data processing systemof FIG. 9 may, for example, a personal computer (PC), workstation,tablet, smartphone or other hand-held wireless device, or any devicehaving similar functionality.

As shown, the data processing system 901 includes a system bus 902 whichis coupled to a microprocessor 903, a Read-Only Memory (ROM) 907, avolatile Random Access Memory (RAM) 905, as well as other nonvolatilememory 906. In the illustrated embodiment, microprocessor 903 is coupledto cache memory 904. System bus 902 can be adapted to interconnect thesevarious components together and also interconnect components 903, 907,905, and 906 to a display controller and display device 908, and toperipheral devices such as input/output (“I/O”) devices 510. Types ofI/O devices can include keyboards, modems, network interfaces, printers,scanners, video cameras, or other devices well known in the art.Typically, I/O devices 510 are coupled to the system bus 902 through I/Ocontrollers 909. In one embodiment the I/O controller 909 includes aUniversal Serial Bus (“USB”) adapter for controlling USB peripherals orother type of bus adapter.

RAM 905 can be implemented as dynamic RAM (“DRAM”) which requires powercontinually in order to refresh or maintain the data in the memory. Theother nonvolatile memory 906 can be a magnetic hard drive, magneticoptical drive, optical drive, DVD RAM, or other type of memory systemthat maintains data after power is removed from the system. While FIG. 3shows that nonvolatile memory 906 as a local device coupled with therest of the components in the data processing system, it will beappreciated by skilled artisans that the described techniques may use anonvolatile memory remote from the system, such as a network storagedevice coupled with the data processing system through a networkinterface such as a modem or Ethernet interface (not shown).

With these embodiments in mind, it will be apparent from thisdescription that aspects of the described techniques may be embodied, atleast in part, in software, hardware, firmware, or any combinationthereof. It should also be understood that embodiments can employvarious computer-implemented functions involving data stored in a dataprocessing system. That is, the techniques may be carried out in acomputer or other data processing system in response executing sequencesof instructions stored in memory. In various embodiments, hardwiredcircuitry may be used independently, or in combination with softwareinstructions, to implement these techniques. For instance, the describedfunctionality may be performed by specific hardware componentscontaining hardwired logic for performing operations, or by anycombination of custom hardware components and programmed computercomponents. The techniques described herein are not limited to anyspecific combination of hardware circuitry and software.

Embodiments herein may also be in the form of computer code stored on acomputer-readable medium. Computer-readable media can also be adapted tostore computer instructions, which when executed by a computer or otherdata processing system, such as data processing system 500, are adaptedto cause the system to perform operations according to the techniquesdescribed herein. Computer-readable media can include any mechanism thatstores information in a form accessible by a data processing device suchas a computer, network device, tablet, smartphone, or any device havingsimilar functionality. Examples of computer-readable media include anytype of tangible article of manufacture capable of storing informationthereon such as a hard drive, floppy disk, DVD, CD-ROM, magnetic-opticaldisk, ROM, RAM, EPROM, EEPROM, flash memory and equivalents thereto, amagnetic or optical card, or any type of media suitable for storingelectronic data. Computer-readable media can also be distributed over anetwork-coupled computer system, which can be stored or executed in adistributed fashion.

It is also understood that the examples and embodiments described hereinare for illustrative purposes only and that various modifications orchanges in light thereof will be suggested to persons skilled in the artand are to be included within the spirit and purview of this applicationand scope of the appended claims.

1. (canceled)
 2. A method of correcting an infrared image, the methodcomprising: providing a processor; receiving the infrared image from acamera, the infrared image comprising a plurality of pixels arranged inan input image array, a first pixel in the plurality of pixels having afirst pixel value and one or more neighbor pixels with one or moreneighbor pixel values, wherein the first pixel and the one or moreneighbor pixels are associated with an object in the infrared image, theone of more neighbor pixels being adjacent to the first pixel in theinput image array; processing the infrared image to generate a processedimage; determining, by the processor, that the processed image is afirst frame: initializing a correction table to an initializedcorrection table; initializing a mean image counter to zero;determining, by the processor, that the camera is moving: determiningthat a motion of the camera is greater than or equal to a motionthreshold: updating the correction table using a motion-based algorithm;and providing an output image based on the updated correction tableusing the motion-based algorithm and the processed image.
 3. The methodof claim 2, further comprising: determining that the motion of thecamera is less than the motion threshold: initializing a mean image tothe processed image; and initializing the mean image counter to zero. 4.The method of claim 3, further comprising: determining that the meanimage counter is greater than a count threshold: updating the correctiontable using a motionless algorithm; and providing the output image basedon the updated correction table using the motionless algorithm and theprocessed image.
 5. The method of claim 3, further comprising:determining that the mean image counter is less than or equal to a countthreshold: providing the output image based on the initializedcorrection table and the processed image.
 6. The method of claim 2,wherein updating the correction table using the motion-based algorithmcomprises: for each correction pixel value, determining that the firstpixel value is not an edge; for each of the one or more neighbor pixelvalues, determining that a neighbor pixel value is not an edge;computing a difference between the first pixel value and the one or moreneighbor pixel values; and determining a correction pixel value based onthe difference.
 7. The method of claim 6, wherein updating thecorrection table using the motion-based algorithm further comprises:adding the difference to a previous neighbor correction value to updatea neighbor correction value; and subtracting the difference from aprevious correction value to update a correction value.
 8. The method ofclaim 2, wherein updating the correction table using the motion-basedalgorithm comprises: for each correction pixel value, determiningcorrection values for the one or more neighbor pixels; computing adifference between the first pixel value and the one or more neighborpixel values; and updating the correction pixel value using thedifference.
 9. The method of claim 8, wherein updating the correctiontable using the motion-based algorithm further comprises: determiningthat an absolute value of the difference is within a range.
 10. Themethod of claim 9, wherein the range is defined by a lower threshold andan upper threshold that are predetermined values.
 11. The method ofclaim 9, wherein updating the correction table using the motion-basedalgorithm further comprises: adding a fraction of the difference to aprevious neighbor correction value to update a neighbor correctionvalue; and subtracting the fraction of the difference from a previouscorrection value to update a correction value.
 12. The method of claim11, wherein the fraction of the difference comprises dividing thedifference by a denominator, wherein the denominator is a predeterminedvalue.
 13. The method of claim 2, wherein the motion threshold isdefined by a plurality of pixel units.
 14. A method of correcting aninfrared image, the method comprising: providing a processor; receivingthe infrared image from a camera, the infrared image comprising aplurality of pixels arranged in an input image array, a first pixel inthe plurality of pixels having a first pixel value and one or moreneighbor pixels with one or more neighbor pixel values, wherein thefirst pixel and the one or more neighbor pixels are associated with anobject in the infrared image, the one of more neighbor pixels beingadjacent to the first pixel in the input image array; processing theinfrared image to generate a processed image; determining, by theprocessor, that the processed image is a first frame: initializing acorrection table to an initialized correction table; initializing a meanimage counter to zero; determining, by the processor, that the camera isnot moving: computing a percentage change in a grayscale value of eachpixel relative to a corresponding pixel in a mean image; marking pixelshaving a percentage change greater than a grayscale threshold as movingpixels; and counting a number of moving pixels.
 15. The method of claim14, further comprising: determining that the number of moving pixels isgreater a change threshold: initializing the mean image to the processedimage; initializing the mean image counter to zero; and providing anoutput image based on the initialized correction table and the processedimage.
 16. The method of claim 14, further comprising: determining thatthe number of moving pixels is less than or equal to a change threshold:initializing the mean image to a sum of the processed image multipliedby a constant and a fraction of a previous mean image; and incrementingthe mean image counter, wherein the constant is less than unity, and asum of the constant and the fraction is equal to unity.
 17. The methodof claim 16, further comprising: determining that the mean image counteris greater than a count threshold: updating the correction table using amotionless algorithm; and providing an output image based on the updatedcorrection table using the motionless algorithm and the processed image.18. The method of claim 17, wherein updating the correction table usingthe motionless algorithm comprises: for each correction pixel value,determining correction values for the one or more neighbor pixels;computing a difference between the first pixel value and the one or moreneighbor pixel values; and updating the correction pixel value using thedifference.
 19. The method of claim 18, wherein updating the correctiontable using the motionless algorithm further comprises: determining thatan absolute value of the difference is within a range.
 20. The method ofclaim 18, wherein updating the correction table using the motionlessalgorithm further comprises: adding a fraction of the difference to aprevious neighbor correction value to update a neighbor correctionvalue; and subtracting the fraction of the difference from a previouscorrection value to update a correction value, wherein the fraction ofthe difference comprises dividing the difference by a denominator,wherein the denominator is a predetermined value.
 21. The method ofclaim 16, further comprising: determining that the mean image counter isless than or equal to a count threshold: providing an output image basedon the initialized correction table and the processed image.